Music Artist Classification with Convolutional Recurrent Neural Networks

TL;DR

This paper demonstrates that using a convolutional recurrent neural network (CRNN) with varying audio clip lengths significantly improves music artist classification accuracy, highlighting the importance of temporal structure in spectrogram features.

Contribution

It introduces the impact of audio clip length on CRNN-based artist classification and empirically demonstrates improved performance over baselines on the artist20 dataset.

Findings

Best model achieves an F1 score of 0.937

Temporal structure enhances classification accuracy

Visualizations show meaningful feature clustering

Abstract

Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This paper revisits artist classification with this new framework and empirically explores the impacts of incorporating temporal structure in the feature representation. To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions. These include audio clip length, which is a novel contribution in this work, and previously identified considerations such as dataset split and feature level. Our results improve upon baseline works, verify the influence of the producer effect on classification performance and demonstrate the trade-offs between audio length and training set size. The best performing model achieves an average F1 score of 0.937 across three independent trials which is a substantial improvement over the corresponding baseline under similar conditions. Additionally, to showcase the effectiveness of the CRNN's feature extraction capabilities, we visualize audio samples at the model's bottleneck layer demonstrating that learned representations segment into clusters belonging to their respective artists.